Part A: Text Classification - Binary Classification

Importing dataset

We have 49 missing reviews text.

Counting number of ratings

Making Rating ready for binary classifying.

Classifying 4,5 as 1(high) and 0,1,2 as 0(low) and disregarding 3.

Preprocessing the text in Review Text column

NAIVE BAYES CLASSIFIER

Accuracy of training set.

Accuracy of testing set.

LOGISTIC REGRESSION

Accuracy of training set.

Accuracy of testing set.

Part B: Text Explainer

LIME ( Local Interpretable Model-agnostic Explanations )

is a novel explanation technique that explains the prediction of any classifier in an interpretable and faithful manner by learning an interpretable model locally around the prediction.

ELI5

It is a python package that is used to inspect ML classifiers and give a explanation to their predictions. It is popularly used to debug algorithms such as sklearn regressors and classifiers, XGBoost, CatBoost, Keras, etc.

Part C: Topic Modeling

Latent Dirichlet Allocation (LDA)